def __plot_output(result, title, save_file, best_output, best_actions):
_, n_iterations = np.array(result).shape
info = result[2]
causal_effect = [round(x, 4) for x in result[3]]
x = np.array(range(0, n_iterations))
right_sub = [i for i in range(n_iterations) if causal_effect[i] > 0.1]
wrong_sub = [i for i in range(n_iterations) if causal_effect[i] <= 0.1]
pltz.set_figure_size(12, 10)
pltz.plot(x, info)
pltz.plot(x[right_sub], info[right_sub], 'go', linewidth=3, markersize=10)
pltz.plot(x[wrong_sub], info[wrong_sub], 'rx', linewidth=3, markersize=10)
# pltz.xlabel('Iteration Number')
pltz.xticks([])
pltz.ylabel('Information Gain')
pltz.title(title)
pltz.legend()
col_labels = [str(i) for i in range(n_iterations)]
row_labels = ['Selected A_i', 'Selected ΔF', 'TE']
table_vals = np.array([best_actions, best_output, causal_effect])
# row_colors = ['red', 'gold', 'green']
my_table = plt.table(cellText=table_vals,
colWidths=[1 / n_iterations] * n_iterations,
rowLabels=row_labels,
colLabels=col_labels,
colLoc='center',
loc='bottom')
plt.subplots_adjust(left=0.2, bottom=0.2)
plt.savefig(data_path + save_file)
plt.show()
def age_show():
# 显示年龄分布情况
now_year = dt.datetime.today().year # 当前的年份
# 计算年龄
df['birth'] = pd.to_datetime(df['userid'].map(fun))
df['Age'] = now_year - df.birth.dt.year
t = df
age2 = t[(t.Age >= 20) & (t.Age < 25)]['Age'].count()
age3 = t[(t.Age >= 25) & (t.Age < 30)]['Age'].count()
age4 = t[(t.Age >= 30) & (t.Age < 35)]['Age'].count()
age5 = t[(t.Age >= 35) & (t.Age < 40)]['Age'].count()
age6 = t[(t.Age >= 40) & (t.Age < 45)]['Age'].count()
age7 = t[(t.Age >= 45) & (t.Age < 50)]['Age'].count()
age8 = t[(t.Age >= 50) & (t.Age < 55)]['Age'].count()
age9 = t[(t.Age >= 55) & (t.Age < 60)]['Age'].count()
age10 = t[(t.Age >= 60) & (t.Age < 80)]['Age'].count()
s = pd.DataFrame({'人数': [age2, age3, age4, age5, age6, age7, age8, age9, age10]},
index=['20-25', '25-30', '30-35', '35-40', '40-45', '45-50', '50-55', '55-60', '60-76'])
s.plot(kind='bar')
plt.title('嘉裕花苑二期项目5#-18#楼1500户申购入围家庭年龄分布')
plt.show()
Age = df.Age.value_counts()
print(Age)
Age = Age.to_frame()
Age = Age.sort_index()
print(Age)
Age.plot(kind='line')
plt.title('嘉裕花苑二期项目5#-18#楼1500户申购入围家庭年龄分布')
plt.show()
def town_count():
town = df['town'].value_counts()
town_df = town.to_frame()
index = town_df.town.index.tolist()
counts = town_df['town'].tolist()
plt.figure(figsize=(9, 6))
# 画线条
l2, = plt.plot(index, counts, color='red', linewidth=1.0, linestyle='--', label='square line')
# 画柱状图
plt.bar(index, counts, alpha=0.9, width=0.55, facecolor='lightskyblue', edgecolor='white', label='one', lw=1)
plt.xticks(rotation=45, fontsize=10)
plt.title('嘉裕花苑二期项目5#-18#楼1500户申购入围家庭所在乡镇')
plt.xlabel("乡镇")
plt.ylabel("人数")
# 设置数字标签
for a, b in zip(index, counts):
plt.text(a, b + 0.05, '%.0f' % b, ha='center', va='bottom', fontsize=10)
plt.show()
draw_pie(index, counts)
def print_others(msg):
# print(msg)
if msg.type == 'Sharing':
response = requests.get(msg.url)
document = PyQuery(response.text)
content = document('p').text()
content = re.sub('[a-zA-Z0-9’!"%&\'^\s+|\s+$()*+,-./:;，。?、…？“”‘’！]+',
"", content)
seg_list = jieba.cut(content, cut_all=True)
result = Counter()
for seg in seg_list:
if len(seg) > 1:
result[seg] = result[seg] + 1
mydic = result.most_common(20)
source_data = {}
for i in mydic:
source_data[i[0]] = i[1]
print(source_data)
# 设置X轴Y轴数据
x_axis = tuple(source_data.keys())
y_axis = tuple(source_data.values())
plt.bar(x_axis, y_axis, width=0.5)
plt.xlabel(u"高频词汇") # 指定x轴描述信息
plt.ylabel(u"频次") # 指定y轴描述信息
plt.title("高频词汇统计表") # 指定图表描述信息
plt.ylim(0, int(y_axis[0])) # 指定Y轴的高度
plt.savefig('my_result.png') # 保存为图片
msg.sender.send_image('my_result.png')
plt.show()
def country():
#print(df.loc[df["town"] == "龙门滩"])
country = df.loc[df["town"] == "葛坑"]['country'].value_counts()
country_df = country.to_frame()
index = country_df.country.index.tolist()
counts = country_df['country'].tolist()
plt.figure(figsize=(9, 6))
plt.rcParams['font.sans-serif'] = ['Arial Unicode MS'] # for Chinese characters
plt.bar(index, counts, alpha=0.9, width=0.55, facecolor='lightskyblue', edgecolor='white', label='one', lw=1)
plt.xticks(rotation=45, fontsize=10)
# 设置数字标签
for a, b in zip(index, counts):
plt.text(a, b + 0.05, '%.0f' % b, ha='center', va='bottom', fontsize=10)
plt.title('嘉裕花苑二期项目5#-18#楼1500户申购入围家庭所在乡镇')
plt.show()
def draw_pie(labels, quants):
# make a square figure
plt.figure(1, figsize=(10, 10))
# For China, make the piece explode a bit
expl = [0]*len(quants) # 第二块即China离开圆心0.1
expl[2] = 0.1
# Colors used. Recycle if not enough.
colors = ["blue", "red", "coral", "green", "yellow", "orange"] # 设置颜色（循环显示）
# Pie Plot
# autopct: format of "percent" string;百分数格式
plt.pie(quants, textprops = {'fontsize':15, 'color':'k'},explode=expl, colors=colors, labels=labels, autopct='%1.1f%%', pctdistance=0.8, shadow=False)
plt.title('嘉裕花苑二期项目5#-18#楼1500户申购入围家庭所在乡镇', bbox={'facecolor': '0.8', 'pad': 5})
plt.show()
# plt.savefig("png.jpg")
plt.close()
@bot.register(Friend)
def print_other(msg):
if msg.type == 'Sharing':
response = requests.get(msg.url)
document = PyQuery(response.text)
content = document('p').text()
content = re.sub('[a-zA-Z0-9’!"%&\'^\s+|\s+$()*+,-./:;，。?、…？“”‘’！]+',"",content)
seg_list = jieba.cut(content,cut_all = True)
result = Counter()
for seg in seg_list:
if len(seg) > 1:
result[seg] = result[seg] + 1
mydic = result.most_common(20)
source_data ={}
for i in mydic:
source_data[i[0]] = i[1]
print(source_data)
#设置X轴Y轴数据
x_axis = tuple(source_data.keys())
y_axis = tuple(source_data.values())
plt.bar(x_axis,y_axis,width = 0.5)
plt.xlabel(u"高频词汇") # 指定X轴描述信息
plt.ylabel(u"频次") #指定y轴描述信息
plt.title("高频词汇统计表") #指定图标描述信息
plt.ylim(0,int(y_axis[0])) #指定Y轴的高度
plt.savefig('my_result.png') #保存为图片
msg.sender.send_image('my_result,png')
plt.show()